Lab 1G

Directions: Follow along with the slides and answer the questions in **red** font in your journal.

- In Lab 1F, we saw how we could
*clean*data to make it easier to use and analyze.- You cleaned a small set of variables from the American Time Use (ATU) survey.
- The process of cleaning and then analyzing data is
*very*common in Data Science.

- In this lab, we’ll learn how we can create frequency tables to detect relationships between categorical variables.
- For the sake of consistency, rather than using the data that you cleaned, you will use the pre-loaded ATU data.
- Use the
`data()`

function to load the`atu_clean`

data file to use in this lab.

- When we’re dealing with categorical variables, we can’t just calculate an
**average**to describe a*typical*value.- (Honestly, what’s the average of categories
*orange*,*apple*and*banana*, for instance?)

- (Honestly, what’s the average of categories
- When trying to describe categorical variables with numbers, we calculate
**frequency tables**

- When it comes to categories, about all you can do is
*count*or*tally*how often each category comes up in the data. - Fill in the blanks below to answer the following:
**How many more***females*than*males*are there in our ATU data?

- Counting the categories of a single variable is nice, but often times we want to make comparisons.
- For example, what if we wanted to answer the question:
**Does one**`gender`

seem to have a higher occurrence of physical challenges than the other? If so, which one and explain your reasoning?

- We could use the following plot to try and answer this question:

- The split bargraph helps us get an idea of the answer to the question, but we need to provide precise values.
**Use a line of code, that’s similar to how we facet plots, to obtain a tally of the number of people with physical challenges and their genders.**

- Recall that there were 1153 more women than men in our data set.
- If there are more women, then we might expect women to have more physical challenges (compared to men).

- Instead of using
*counts*we use*percentages*. - Include:
`format = "percent"`

as an option to the code you used to make your 2-way frequency table. Then answer this question again:**Does one**`gender`

seem to have a higher occurrence of physical challenges than the other? If so, which one and explain your reasoning?**Did your answer change from before? Why?**

- It’s often helpful to display totals in our 2-way frequency tables.
- To include them, include
`margins = TRUE`

as an option in the tally function.

- To include them, include

- There is as difference between
`phys_challenge | gender`

and`gender | phys_challenge`

.

```
## gender
## phys_challenge Male Female
## No difficulty 4140 5048
## Has difficulty 530 775
## Total 4670 5823
```

```
## phys_challenge
## gender No difficulty Has difficulty
## Male 4140 530
## Female 5048 775
## Total 9188 1305
```

At first glance, the two-way frequency tables might look similar (especially when the

`margin`

option is excluded). Notice, however, that the totals are different.The totals are telling us that

`R`

calculates conditional frequencies by column!What does this mean?

- In the first two-way frequency table the groups being compared are
`Male`

and`Female`

on the distribution of physical challenges. - In the second two-way frequency table the groups being compared are the people with
`No difficulty`

and those that`Has difficulty`

on the distribution of gender.

- In the first two-way frequency table the groups being compared are
**Add the option**`format = "percent"`

to the first tally function. How were the percents calculated? Interpret what they mean.

**Describe what happens if you create a 2-way frequency table with a numerical variable and a categorical variable.****How are the types of statistical questions that 2-way frequency tables can answer different than 1-way frequency tables?****Which gender has a higher rate of***part time employment*?